svm_learn_main.c

机器学习文本分类的SVM算法实现

    learn_parm->type=CLASSIFICATION;
  }
  else if(strcmp(type,"r")==0) {
    learn_parm->type=REGRESSION;
  }
  else if(strcmp(type,"p")==0) {
    learn_parm->type=RANKING;
  }
  else if(strcmp(type,"o")==0) {
    learn_parm->type=OPTIMIZATION;
  }
  else if(strcmp(type,"s")==0) {
    learn_parm->type=OPTIMIZATION;
    learn_parm->sharedslack=1;
  }
  else {
    printf("\nUnknown type '%s': Valid types are 'c' (classification), 'r' regession, and 'p' preference ranking.\n",type);
    wait_any_key();
    print_help();
    exit(0);
  }    
  if((learn_parm->skip_final_opt_check) 
     && (kernel_parm->kernel_type == LINEAR)) {
    printf("\nIt does not make sense to skip the final optimality check for linear kernels.\n\n");
    learn_parm->skip_final_opt_check=0;
  }    
  if((learn_parm->skip_final_opt_check) 
     && (learn_parm->remove_inconsistent)) {
    printf("\nIt is necessary to do the final optimality check when removing inconsistent \nexamples.\n");
    wait_any_key();
    print_help();
    exit(0);
  }    
  if((learn_parm->svm_maxqpsize<2)) {
    printf("\nMaximum size of QP-subproblems not in valid range: %ld [2..]\n",learn_parm->svm_maxqpsize); 
    wait_any_key();
    print_help();
    exit(0);
  }
  if((learn_parm->svm_maxqpsize<learn_parm->svm_newvarsinqp)) {
    printf("\nMaximum size of QP-subproblems [%ld] must be larger than the number of\n",learn_parm->svm_maxqpsize); 
    printf("new variables [%ld] entering the working set in each iteration.\n",learn_parm->svm_newvarsinqp); 
    wait_any_key();
    print_help();
    exit(0);
  }
  if(learn_parm->svm_iter_to_shrink<1) {
    printf("\nMaximum number of iterations for shrinking not in valid range: %ld [1,..]\n",learn_parm->svm_iter_to_shrink);
    wait_any_key();
    print_help();
    exit(0);
  }
  if(learn_parm->svm_c<0) {
    printf("\nThe C parameter must be greater than zero!\n\n");
    wait_any_key();
    print_help();
    exit(0);
  }
  if(learn_parm->transduction_posratio>1) {
    printf("\nThe fraction of unlabeled examples to classify as positives must\n");
    printf("be less than 1.0 !!!\n\n");
    wait_any_key();
    print_help();
    exit(0);
  }
  if(learn_parm->svm_costratio<=0) {
    printf("\nThe COSTRATIO parameter must be greater than zero!\n\n");
    wait_any_key();
    print_help();
    exit(0);
  }
  if(learn_parm->epsilon_crit<=0) {
    printf("\nThe epsilon parameter must be greater than zero!\n\n");
    wait_any_key();
    print_help();
    exit(0);
  }
  if(learn_parm->rho<0) {
    printf("\nThe parameter rho for xi/alpha-estimates and leave-one-out pruning must\n");
    printf("be greater than zero (typically 1.0 or 2.0, see T. Joachims, Estimating the\n");
    printf("Generalization Performance of an SVM Efficiently, ICML, 2000.)!\n\n");
    wait_any_key();
    print_help();
    exit(0);
  }
  if((learn_parm->xa_depth<0) || (learn_parm->xa_depth>100)) {
    printf("\nThe parameter depth for ext. xi/alpha-estimates must be in [0..100] (zero\n");
    printf("for switching to the conventional xa/estimates described in T. Joachims,\n");
    printf("Estimating the Generalization Performance of an SVM Efficiently, ICML, 2000.)\n");
    wait_any_key();
    print_help();
    exit(0);
  }
}

void wait_any_key()
{
  printf("\n(more)\n");
  (void)getc(stdin);
}

void print_help()
{
  printf("\nSVM-light %s: Support Vector Machine, learning module     %s\n",VERSION,VERSION_DATE);
  copyright_notice();
  printf("   usage: svm_learn [options] example_file model_file\n\n");
  printf("Arguments:\n");
  printf("         example_file-> file with training data\n");
  printf("         model_file  -> file to store learned decision rule in\n");

  printf("General options:\n");
  printf("         -?          -> this help\n");
  printf("         -v [0..3]   -> verbosity level (default 1)\n");
  printf("Learning options:\n");
  printf("         -z {c,r,p}  -> select between classification (c), regression (r),\n");
  printf("                        and preference ranking (p) (default classification)\n");
  printf("         -c float    -> C: trade-off between training error\n");
  printf("                        and margin (default [avg. x*x]^-1)\n");
  printf("         -w [0..]    -> epsilon width of tube for regression\n");
  printf("                        (default 0.1)\n");
  printf("         -j float    -> Cost: cost-factor, by which training errors on\n");
  printf("                        positive examples outweight errors on negative\n");
  printf("                        examples (default 1) (see [4])\n");
  printf("         -b [0,1]    -> use biased hyperplane (i.e. x*w+b>0) instead\n");
  printf("                        of unbiased hyperplane (i.e. x*w>0) (default 1)\n");
  printf("         -i [0,1]    -> remove inconsistent training examples\n");
  printf("                        and retrain (default 0)\n");
  printf("Performance estimation options:\n");
  printf("         -x [0,1]    -> compute leave-one-out estimates (default 0)\n");
  printf("                        (see [5])\n");
  printf("         -o ]0..2]   -> value of rho for XiAlpha-estimator and for pruning\n");
  printf("                        leave-one-out computation (default 1.0) (see [2])\n");
  printf("         -k [0..100] -> search depth for extended XiAlpha-estimator \n");
  printf("                        (default 0)\n");
  printf("Transduction options (see [3]):\n");
  printf("         -p [0..1]   -> fraction of unlabeled examples to be classified\n");
  printf("                        into the positive class (default is the ratio of\n");
  printf("                        positive and negative examples in the training data)\n");
  printf("Kernel options:\n");
  printf("         -t int      -> type of kernel function:\n");
  printf("                        0: linear (default)\n");
  printf("                        1: polynomial (s a*b+c)^d\n");
  printf("                        2: radial basis function exp(-gamma ||a-b||^2)\n");
  printf("                        3: sigmoid tanh(s a*b + c)\n");
  printf("                        4: user defined kernel from kernel.h\n");
  printf("         -d int      -> parameter d in polynomial kernel\n");
  printf("         -g float    -> parameter gamma in rbf kernel\n");
  printf("         -s float    -> parameter s in sigmoid/poly kernel\n");
  printf("         -r float    -> parameter c in sigmoid/poly kernel\n");
  printf("         -u string   -> parameter of user defined kernel\n");
  printf("Optimization options (see [1]):\n");
  printf("         -q [2..]    -> maximum size of QP-subproblems (default 10)\n");
  printf("         -n [2..q]   -> number of new variables entering the working set\n");
  printf("                        in each iteration (default n = q). Set n<q to prevent\n");
  printf("                        zig-zagging.\n");
  printf("         -m [5..]    -> size of cache for kernel evaluations in MB (default 40)\n");
  printf("                        The larger the faster...\n");
  printf("         -e float    -> eps: Allow that error for termination criterion\n");
  printf("                        [y [w*x+b] - 1] >= eps (default 0.001)\n");
  printf("         -y [0,1]    -> restart the optimization from alpha values in file\n");
  printf("                        specified by -a option. (default 0)\n");
  printf("         -h [5..]    -> number of iterations a variable needs to be\n"); 
  printf("                        optimal before considered for shrinking (default 100)\n");
  printf("         -f [0,1]    -> do final optimality check for variables removed\n");
  printf("                        by shrinking. Although this test is usually \n");
  printf("                        positive, there is no guarantee that the optimum\n");
  printf("                        was found if the test is omitted. (default 1)\n");
  printf("         -y string   -> if option is given, reads alphas from file with given\n");
  printf("                        and uses them as starting point. (default 'disabled')\n");
  printf("         -# int      -> terminate optimization, if no progress after this\n");
  printf("                        number of iterations. (default 100000)\n");
  printf("Output options:\n");
  printf("         -l string   -> file to write predicted labels of unlabeled\n");
  printf("                        examples into after transductive learning\n");
  printf("         -a string   -> write all alphas to this file after learning\n");
  printf("                        (in the same order as in the training set)\n");
  wait_any_key();
  printf("\nMore details in:\n");
  printf("[1] T. Joachims, Making Large-Scale SVM Learning Practical. Advances in\n");
  printf("    Kernel Methods - Support Vector Learning, B. Sch鰈kopf and C. Burges and\n");
  printf("    A. Smola (ed.), MIT Press, 1999.\n");
  printf("[2] T. Joachims, Estimating the Generalization performance of an SVM\n");
  printf("    Efficiently. International Conference on Machine Learning (ICML), 2000.\n");
  printf("[3] T. Joachims, Transductive Inference for Text Classification using Support\n");
  printf("    Vector Machines. International Conference on Machine Learning (ICML),\n");
  printf("    1999.\n");
  printf("[4] K. Morik, P. Brockhausen, and T. Joachims, Combining statistical learning\n");
  printf("    with a knowledge-based approach - A case study in intensive care  \n");
  printf("    monitoring. International Conference on Machine Learning (ICML), 1999.\n");
  printf("[5] T. Joachims, Learning to Classify Text Using Support Vector\n");
  printf("    Machines: Methods, Theory, and Algorithms. Dissertation, Kluwer,\n");
  printf("    2002.\n\n");
}